FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908460414664704 |
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| author | Wang, Zheng Sun, Xianbing Wu, Shengyi Zhan, Jiahui Si, Jianlou Zhang, Chi Zhang, Liqing Zhang, Jianfu |
| author_facet | Wang, Zheng Sun, Xianbing Wu, Shengyi Zhan, Jiahui Si, Jianlou Zhang, Chi Zhang, Liqing Zhang, Jianfu |
| contents | Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel approach to the person-to-person try-on task. Unlike the garment-to-person try-on task, the person-to-person task only involves two input images: one depicting the target person and the other showing the garment worn by a different individual. The goal is to generate a realistic combination of the target person with the desired garment. To this end, we propose Flattening-and-Warping Virtual Try-On (\textbf{FW-VTON}), a method that operates in three stages: (1) extracting the flattened garment image from the source image; (2) warping the garment to align with the target pose; and (3) integrating the warped garment seamlessly onto the target person. To overcome the challenges posed by the lack of high-quality datasets for this task, we introduce a new dataset specifically designed for person-to-person try-on scenarios. Experimental evaluations demonstrate that FW-VTON achieves state-of-the-art performance, with superior results in both qualitative and quantitative assessments, and also excels in garment extraction subtasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on Wang, Zheng Sun, Xianbing Wu, Shengyi Zhan, Jiahui Si, Jianlou Zhang, Chi Zhang, Liqing Zhang, Jianfu Computer Vision and Pattern Recognition Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel approach to the person-to-person try-on task. Unlike the garment-to-person try-on task, the person-to-person task only involves two input images: one depicting the target person and the other showing the garment worn by a different individual. The goal is to generate a realistic combination of the target person with the desired garment. To this end, we propose Flattening-and-Warping Virtual Try-On (\textbf{FW-VTON}), a method that operates in three stages: (1) extracting the flattened garment image from the source image; (2) warping the garment to align with the target pose; and (3) integrating the warped garment seamlessly onto the target person. To overcome the challenges posed by the lack of high-quality datasets for this task, we introduce a new dataset specifically designed for person-to-person try-on scenarios. Experimental evaluations demonstrate that FW-VTON achieves state-of-the-art performance, with superior results in both qualitative and quantitative assessments, and also excels in garment extraction subtasks. |
| title | FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16010 |